Repository of Reproducible Computations

Free Statistics

of Irreproducible Research!

Author's title

Author

*The author of this computation has been verified*

R Software Module

rwasp_surveyscores.wasp

Title produced by software

Survey Scores

Date of computation

Sun, 07 Sep 2008 08:04:56 -0600

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Sep/07/t1220796393qktqk00wbmi19d7.htm/, Retrieved Sat, 18 May 2024 14:20:52 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=14334, Retrieved Sat, 18 May 2024 14:20:52 +0000

QR Codes:

Paste this QR Code to cite your computation.

Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

292

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Survey Scores] [E-Learn 2008 table 3] [2008-09-07 14:04:56] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]

Feedback Forum

Post a new message

Dataseries X:

Download CSV

Histogram

Boxplots

0	0	1	1	1	1	1	1	-1	0	-1	0	1	1	0	1	1	0	0	1	1	2	1	1	1	2	1	2	1	1	1	0	1
1	1	0	0	0	2	2	-1	1	1	1	1	1	1	1	1	1	1	2	2	2	1	1	1	0	2	2	2	2	1	1	1	1
1	1	1	1	0	0	2	1	0	0	1	1	0	1	1	0	0	1	1	2	1	1	1	1	1	1	2	2	1	1	1	1	0
1	0	2	1	1	1	-1	0	-1	-1	0	0	1	1	1	0	-1	1	1	2	1	2	1	1	0	2	1	1	1	0	-1	2	1
2	1	1	1	1	1	1	1	-1	1	2	2	0	0	1	2	2	1	1	1	1	0	0	1	0	2	2	1	2	1	0	1	1
1	1	1	0	0	1	1	1	-1	0	0	0	0	1	1	1	1	2	1	2	1	1	0	1	-1	1	2	2	1	1	1	1	1
1	1	0	0	0	-1	1	-1	-1	-1	1	1	-2	0	0	1	1	0	1	-1	-1	0	-1	1	-2	1	0	1	0	1	1	1	-1
2	1	2	1	1	1	1	0	0	2	1	1	0	1	1	1	1	2	1	1	1	0	-1	1	0	1	1	1	1	2	1	1	1
1	0	1	0	1	1	0	1	0	0	0	1	0	0	1	0	0	0	1	1	0	0	1	1	0	1	1	0	1	1	0	0	0
1	1	0	0	0	-1	0	-1	0	-1	0	0	-1	0	0	-1	-1	-1	0	0	0	0	0	1	0	1	0	0	0	-1	0	1	1
0	0	1	1	1	0	1	0	-1	0	2	0	0	0	1	-1	-1	-1	0	0	1	1	1	-1	0	-2	1	1	0	1	0	-2	1
2	2	1	0	1	2	1	1	2	1	0	0	0	1	1	2	2	1	2	2	2	2	2	1	0	2	1	2	2	2	1	2	1
1	1	1	1	2	1	2	1	1	1	1	2	1	1	2	-1	-1	1	2	1	2	1	1	2	0	1	NA	2	1	1	1	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	0	1	1	1	1	-1	1	0	1	1	1	0	1	1	-1	1	1	1	2	1	0	1	-1	-1	0	1	0	0	1	1	0	0
-1	-1	1	-1	-1	0	-1	-1	0	0	1	0	0	1	1	-2	-2	1	-1	2	1	2	1	1	1	2	2	2	1	-1	-1	1	0
2	1	0	1	1	1	-1	1	1	1	0	1	0	1	1	1	-1	1	1	-1	0	-1	0	1	-2	1	-1	0	-1	1	1	1	-1
2	2	1	1	2	2	2	1	-1	1	1	1	1	1	0	NA	NA	1	1	2	2	0	-1	1	1	1	NA	0	2	1	-1	1	1
2	2	1	0	2	2	2	1	0	2	2	2	-1	1	2	2	2	2	2	1	2	2	2	2	1	1	2	2	2	0	2	2	2
0	0	1	2	0	1	1	2	-1	0	-1	1	0	2	-1	-1	-1	1	1	2	1	1	2	1	1	1	2	1	0	1	0	0	1
1	1	0	0	0	1	1	1	-1	0	0	0	0	0	1	1	0	1	1	2	1	1	1	0	-1	0	1	1	0	0	0	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	1	1	1	2	1	0	0	0	1	0	0	0	1	0	0	1	1	0	1	0	0	1	1	1	0	1	1	1	1	1	1
1	1	1	0	1	1	1	1	0	0	0	0	0	1	0	1	1	1	1	2	1	1	1	1	1	1	1	1	0	1	1	0	1
0	0	1	-1	0	1	1	1	-2	0	0	1	-1	0	1	1	0	0	0	2	2	2	2	1	1	1	2	2	1	1	1	1	1
2	2	1	1	1	2	0	1	-1	0	0	0	0	0	1	1	1	1	2	2	1	1	1	1	-1	1	1	1	2	1	0	1	0
1	0	0	0	0	0	NA	0	-1	0	0	0	0	0	1	0	0	1	0	-1	1	0	1	2	1	1	1	1	1	1	1	1	-1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
0	0	1	0	0	0	0	1	-1	0	1	-1	-1	0	1	1	1	1	0	0	0	1	1	1	1	2	NA	2	1	0	1	1	0
0	1	2	1	1	0	1	0	0	-1	0	0	0	1	0	1	1	0	1	0	1	1	0	1	-1	1	0	-1	0	0	-2	-1	-1
1	2	1	1	1	2	2	2	0	1	0	1	1	1	1	0	1	2	1	0	0	1	0	1	1	1	1	1	1	1	1	2	2
2	2	1	1	1	2	0	1	2	2	2	1	1	2	1	2	2	2	2	2	2	2	0	2	2	2	0	2	2	2	1	2	1
2	2	2	2	2	1	2	1	1	2	1	1	0	0	1	1	1	1	2	2	1	2	2	1	2	2	2	2	2	2	2	2	1
0	0	1	0	0	1	1	1	-1	0	-1	0	0	1	1	0	0	0	1	-1	0	-1	0	2	-1	1	0	0	-1	1	1	1	1
1	1	1	0	1	1	1	1	-1	1	1	1	1	1	0	-1	0	1	1	0	0	1	0	1	1	1	0	0	0	1	1	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	0	2	1	1	0	1	0	1	1	0	-1	-1	0	-1	1	1	1	1	-2	0	-1	-1	1	1	2	-1	1	0	1	1	2	1
1	2	1	1	1	1	2	1	-1	1	0	1	0	1	1	1	1	2	2	2	1	1	0	0	1	2	1	0	1	1	1	1	0
2	1	1	2	1	2	0	1	-1	1	1	2	1	1	0	-1	-1	1	2	1	2	2	2	1	1	2	2	2	1	1	1	2	1
1	0	1	0	1	0	1	1	0	1	0	0	1	1	1	1	0	1	1	0	1	0	0	1	1	2	0	0	0	0	1	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	2	1	1	1	2	2	1	-1	0	1	1	0	-1	0	2	2	1	1	0	0	1	1	1	1	1	1	2	0	1	2	1	0
1	0	1	0	1	1	0	0	-1	0	0	1	1	0	1	1	0	1	1	0	1	0	0	0	1	1	-1	0	0	0	1	1	0
1	1	1	0	0	1	0	1	0	1	1	0	1	0	1	1	0	1	1	0	0	0	0	1	0	1	0	2	0	1	1	1	0
1	1	0	0	0	0	1	0	-1	0	-1	0	0	-1	1	0	-1	-1	0	-1	-1	-1	0	0	1	1	-1	0	-1	-1	0	0	1
1	1	1	1	0	2	1	2	0	1	1	2	1	1	1	2	1	1	1	2	1	1	1	1	0	1	NA	1	1	1	1	1	1
1	1	1	0	0	1	0	0	-1	-1	0	-1	0	0	1	1	0	1	1	2	1	0	1	1	0	1	1	1	0	0	1	1	1
1	2	-1	-1	-1	1	1	1	1	1	1	-1	-2	-2	1	1	1	-1	0	-1	-1	1	-1	1	-1	2	-1	1	-1	0	0	1	1
1	1	1	0	0	1	0	1	0	1	1	0	-1	1	1	1	0	0	1	2	1	0	-1	1	-1	1	NA	2	0	1	-1	1	0
1	1	0	0	1	1	1	0	0	0	1	0	-1	0	1	1	1	1	1	0	1	2	1	0	-1	2	NA	2	0	1	1	0	1
1	1	2	1	1	2	1	2	0	1	1	0	0	1	1	0	1	1	1	2	1	0	-1	1	0	2	2	1	2	2	1	2	2
0	1	1	0	0	1	1	1	-1	1	-1	1	0	0	1	0	0	1	1	1	1	0	0	1	1	0	1	1	1	1	1	0	-1
1	1	1	0	0	0	1	0	0	0	1	1	0	1	1	0	0	1	1	2	0	2	1	2	0	0	2	1	0	0	1	0	0
2	1	1	1	1	1	1	0	0	1	-1	0	0	1	1	-2	-1	1	1	2	1	1	1	-1	1	1	1	2	1	2	1	2	1
1	1	2	2	2	1	1	-1	-1	1	1	2	1	0	1	2	1	1	2	0	1	1	1	2	1	2	0	2	1	1	1	2	2
1	1	1	1	1	1	1	1	0	0	0	1	0	0	0	1	1	1	1	0	1	1	1	1	0	1	1	1	1	1	1	0	1
1	2	1	2	2	2	2	1	-1	0	0	1	1	1	2	1	1	2	1	1	2	1	1	2	-1	2	2	2	1	1	1	1	-1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	0	1	0	0	2	1	1	0	0	1	1	0	1	1	-1	1	2	1	2	1	1	0	0	2	2	1	1	2	0	1	2	0
1	1	2	1	1	1	0	0	0	1	0	0	-1	0	1	0	0	1	2	2	2	1	1	1	1	2	1	1	2	1	1	1	1
1	1	1	1	1	1	1	1	-1	1	0	1	0	1	1	1	1	1	1	-1	0	0	0	1	-1	1	0	1	0	1	1	1	1
1	1	1	1	1	1	0	1	1	1	1	2	0	1	1	1	1	1	1	1	1	1	1	1	1	2	2	1	1	1	1	1	1
1	1	2	1	1	2	2	2	0	0	1	0	0	1	1	1	1	1	2	2	2	1	1	0	0	1	NA	1	NA	1	0	1	0
1	0	0	1	0	0	1	1	1	0	0	1	0	1	1	-1	-1	0	1	-1	1	1	1	1	0	2	1	2	1	1	0	1	2
0	-1	1	0	0	-1	0	-1	-1	-1	-1	0	0	0	1	1	0	0	1	1	1	0	0	0	0	1	0	1	1	1	1	1	1
0	-1	0	0	0	-1	1	0	0	1	0	1	1	0	-1	-2	-2	1	0	-1	-1	1	-1	1	-1	-1	-1	0	-1	0	0	-1	-1
1	2	1	2	1	1	0	1	0	0	0	0	0	0	1	1	1	2	2	2	2	1	1	1	0	1	1	0	1	0	1	1	1
0	1	1	0	1	0	1	0	-1	0	1	1	0	1	1	2	1	0	1	1	1	0	-1	0	0	2	1	1	0	1	0	1	1
0	-1	-1	0	-1	-1	-1	-1	1	-1	0	-1	-1	0	-1	0	0	0	-1	-2	-1	0	-2	0	-1	0	-2	0	-1	0	1	1	-1
1	0	2	2	1	1	0	1	0	1	1	1	2	2	2	1	1	2	1	2	2	1	1	1	1	2	2	2	2	1	0	1	0
0	1	0	0	0	0	0	0	-1	-1	-1	0	-1	0	-1	0	0	-1	0	0	0	1	1	0	-1	0	0	1	0	0	0	1	1
0	-1	1	0	0	0	-1	-2	0	0	0	-1	1	-1	-1	-2	-2	1	0	-2	0	-1	-2	0	-2	1	1	0	0	1	1	2	2
2	1	1	1	0	1	2	1	0	1	1	0	1	1	0	2	1	1	1	2	2	2	2	1	0	1	1	1	1	1	0	2	0
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	1	1	1	0	0	1	2	0	1	1	1	1	1	2	1	1	0	1	1	1	1	0	0	1	1	1	1	1	0	1	0
1	1	1	0	0	1	-1	0	-1	0	0	1	0	0	1	1	1	0	1	-1	0	0	0	1	1	1	-1	1	1	0	1	1	-1
1	1	1	1	1	1	1	0	0	1	0	0	0	1	1	1	1	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
0	1	1	-1	1	2	-1	0	-1	0	1	1	0	-1	0	1	1	1	0	0	1	2	2	0	1	2	0	2	0	0	1	2	2
-1	-1	-1	-2	-1	-1	-1	0	1	0	0	-1	0	-1	-1	0	0	-2	-1	-1	-1	1	0	-1	-1	1	-1	1	-1	0	1	0	1
2	2	1	1	1	1	2	0	1	1	1	1	0	0	1	2	2	2	2	2	2	2	2	2	1	1	2	2	2	1	1	2	1
1	1	1	0	0	1	1	0	0	1	1	0	0	2	1	1	1	1	1	2	2	1	1	1	1	1	1	2	1	0	1	1	0
1	1	1	1	1	1	1	1	1	1	1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	NA	1	1	1	1	1	1
2	2	2	2	2	2	1	2	1	2	1	1	1	1	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2	2
1	1	1	1	1	2	2	1	0	0	1	2	1	1	1	1	1	1	1	2	1	1	0	1	1	1	2	1	1	1	1	1	1
1	0	0	0	-1	-1	-1	-1	-1	-1	0	0	0	0	0	1	0	0	0	-1	-1	-1	-1	0	1	1	NA	0	NA	0	0	1	-1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	0	0	0	1	1	1	-1	0	1	1	1	2	1	1	1	1	1	0	1	1	0	0	0	-1	1	1	1	1	1	0	0
1	1	1	1	1	1	1	1	0	1	0	0	0	0	0	1	1	1	1	1	1	0	1	1	0	1	1	1	1	1	-1	1	1
1	1	1	1	1	1	1	1	0	0	0	0	0	1	1	1	1	1	1	2	2	1	1	1	0	1	1	1	1	1	0	1	0
-1	-1	0	0	1	0	0	1	2	0	0	1	-1	-1	1	0	0	-1	0	2	1	1	1	0	-2	2	2	1	0	0	0	1	2
1	1	0	1	0	0	1	0	-1	-1	-2	0	-1	0	-1	1	1	0	0	-1	0	1	1	1	2	2	1	2	0	0	-1	0	1
1	1	0	-1	-1	0	1	0	0	1	1	1	-1	-1	1	1	1	0	0	-1	-1	0	0	1	2	1	0	0	0	1	1	1	0
1	1	1	1	1	0	1	0	-1	1	1	0	0	0	0	1	1	0	1	1	1	2	2	0	1	1	0	2	0	1	1	2	0
2	1	2	1	2	2	2	2	0	1	0	1	1	1	1	1	1	2	2	2	2	2	2	2	1	2	2	2	2	2	1	2	2
1	1	0	0	0	0	0	1	-1	0	0	1	0	0	1	1	1	1	1	0	0	1	1	0	0	0	-1	1	1	0	2	1	1
1	1	1	1	1	0	1	2	-1	0	1	0	0	1	0	-1	-1	1	1	0	1	1	1	1	1	1	0	1	1	1	0	1	-1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	2	0	1	2	2	1	1	-1	-1	-1	1	1	2	1	1	1	1	1	2	2	1	0	1	0	2	0	1	1	1	1	2	-1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	0	1	0	1	0	1	0	1	0	1	0	1	1	1	0	1	1	1	0	1	0	0	1	-1	0	1	1	0	1	-1	0	0
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	1	0	1	1	1	1	0	1	1	1	0	1	1	-1	-1	0	1	0	1	0	-1	1	0	1	1	0	1	1	1	1	1
1	1	1	1	0	0	0	0	-1	0	0	0	0	0	0	0	0	1	1	1	1	1	1	1	1	0	1	1	1	0	1	1	0
1	2	1	1	1	1	1	0	0	0	1	1	0	0	1	1	1	1	1	0	0	0	1	0	1	1	0	0	1	1	1	1	1
1	1	1	1	1	1	1	1	1	1	1	0	1	1	1	0	0	1	1	0	1	1	0	1	0	1	1	1	0	1	1	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
0	0	1	0	1	-1	-1	1	-1	0	1	1	0	0	1	1	0	1	1	0	1	-1	0	1	-1	1	NA	1	1	1	1	1	1
0	1	2	1	2	-1	0	2	0	0	0	1	0	2	0	-1	-1	1	1	2	2	0	-1	1	1	2	2	2	1	1	1	1	0
2	2	2	1	1	1	1	2	0	1	0	1	0	0	1	-1	0	1	1	0	1	1	1	1	1	1	0	1	1	1	1	1	0
1	1	0	0	0	1	1	1	-1	0	0	0	-1	0	1	1	1	0	1	0	0	0	0	0	1	1	0	1	0	1	0	1	0
1	0	0	0	1	1	1	0	-1	-1	2	1	0	1	1	-1	0	1	1	1	1	2	1	1	1	2	1	1	0	1	1	1	-1
0	1	1	0	0	2	1	0	-1	0	1	0	0	1	1	0	0	1	2	1	1	2	2	2	-1	1	2	2	1	0	0	1	2
0	0	1	0	0	1	0	0	1	1	0	-1	0	1	0	1	1	0	1	1	1	1	0	1	0	2	0	1	1	1	0	1	2
2	2	2	1	1	2	2	2	1	0	1	0	0	1	1	1	1	1	1	0	1	1	0	1	1	2	0	2	1	1	2	2	2
1	1	0	-2	0	1	-1	1	1	1	1	-1	-2	-1	1	-1	-1	0	0	-1	1	-2	-1	0	1	-1	-1	-1	-1	0	-1	0	1
0	0	0	1	0	1	1	0	-2	1	1	1	0	0	1	1	1	-1	0	1	0	0	1	1	0	0	1	1	0	1	1	1	-1
0	0	1	1	0	1	1	1	1	0	0	0	0	1	1	0	0	1	1	1	0	1	1	0	0	1	1	1	1	1	1	0	2
-1	0	-1	-1	-1	0	1	0	-1	-1	0	1	-2	-1	-1	1	-1	1	0	-2	0	1	-1	0	2	0	-2	2	-1	1	2	2	2
1	1	2	1	1	1	0	1	0	1	1	1	0	1	2	2	2	1	2	2	2	2	1	1	2	1	2	1	1	1	2	2	0
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	2	1	1	2	1	1	1	2	1	2	0	1	1	1	1	2	2	1	1	1	1	2	1	1	1	2	1	1	1	2	1
1	1	0	1	2	2	1	0	-1	0	0	1	0	1	0	-1	0	0	1	1	0	0	1	2	0	1	0	1	0	1	1	0	-1
1	1	1	0	0	1	0	1	0	0	1	0	-1	0	0	1	0	1	1	0	0	1	1	1	1	2	0	1	1	0	1	0	0
1	0	1	1	1	1	1	1	0	0	1	1	1	1	1	1	1	1	1	-1	1	1	0	1	1	1	0	0	0	1	1	1	1
1	1	1	1	1	0	0	0	-2	-1	-2	-1	-2	0	-1	-1	-2	0	1	1	1	1	0	0	2	2	1	1	-2	1	-1	2	1
1	1	1	0	0	0	1	0	0	1	1	0	0	1	2	1	0	1	1	2	0	1	1	2	0	1	0	2	1	1	1	1	2
2	2	2	2	2	2	2	1	0	0	0	1	1	1	1	0	1	1	1	1	0	1	-1	0	1	1	0	1	1	0	1	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
0	0	0	0	1	0	0	1	0	1	1	1	0	0	0	1	1	0	1	0	2	2	1	1	0	2	2	2	0	0	0	0	0
0	0	-1	0	-1	-1	-1	-1	0	1	1	1	0	0	1	1	0	1	0	0	1	1	1	1	0	2	0	1	1	0	1	1	2
1	1	2	2	2	1	2	2	0	1	0	1	1	2	0	0	0	1	1	2	2	2	2	1	0	2	2	2	1	1	1	2	2
2	1	1	1	1	1	2	1	0	1	0	1	0	1	1	2	2	1	1	1	0	1	1	2	1	2	0	1	1	1	2	2	2
2	2	0	0	0	1	2	2	2	2	0	0	1	1	2	1	1	2	0	2	0	0	0	0	1	1	-1	0	1	0	0	1	1
2	2	2	2	2	2	2	2	-1	2	2	1	-1	-1	1	1	0	1	1	2	0	2	0	2	1	2	1	1	0	2	2	1	2
0	1	1	0	0	1	2	1	-2	0	0	1	0	1	1	0	0	1	1	-1	-1	0	-1	2	0	2	0	0	-1	1	-1	2	2
1	1	0	-1	0	0	1	-1	2	1	1	0	-1	0	-1	0	0	1	1	0	0	1	0	1	1	2	0	2	0	1	1	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	0	-1	0	-1	2	-1	-1	1	-1	0	-2	-1	1	0	0	1	1	-1	-2	1	0	0	1	1	1	2	-1	1	0	0	1
1	1	1	1	1	1	1	0	0	1	0	1	1	1	0	0	0	1	1	1	0	1	-1	0	1	1	0	0	1	0	-1	1	1
1	1	1	1	0	1	1	1	-1	1	0	0	1	1	1	1	1	1	1	1	1	1	1	1	-2	0	1	1	1	1	1	1	1
1	2	2	1	1	2	0	1	-1	0	1	2	1	2	0	-1	-1	2	2	2	2	1	2	2	2	2	2	2	1	2	2	1	2
1	1	2	1	2	1	1	2	0	0	1	1	1	1	1	1	1	2	1	1	2	2	0	1	0	1	2	2	2	1	0	0	1
2	1	1	0	1	1	2	1	0	-1	0	1	0	1	2	2	0	0	2	1	1	2	0	1	2	1	0	2	0	1	1	2	0
1	2	1	1	1	1	2	1	0	1	1	0	1	1	1	0	0	1	1	1	2	1	1	1	1	1	0	1	1	1	1	1	2
1	1	-1	0	0	1	2	-1	-2	1	1	0	0	0	-1	-1	-1	1	1	1	1	-1	-1	0	2	1	1	0	0	1	1	1	1
1	1	1	0	0	1	1	1	1	1	1	1	0	1	0	1	0	1	1	1	0	0	0	1	1	0	0	0	1	1	1	1	1
1	1	1	0	1	1	1	1	0	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
1	0	1	-1	0	1	0	0	0	1	1	0	0	0	1	1	0	1	1	1	-1	1	1	1	0	1	-1	1	0	0	-1	2	2
1	0	1	0	1	1	0	0	0	1	1	1	1	1	0	0	0	1	1	1	0	0	0	1	1	1	0	0	0	1	1	1	1
1	0	1	1	0	1	0	0	0	1	1	0	0	1	1	1	1	1	1	1	0	0	1	1	1	1	1	0	1	1	1	1	0
1	1	0	0	0	1	1	0	-1	-1	0	-1	0	0	1	1	0	-1	1	0	-1	0	0	-1	-1	-1	-1	0	1	0	-1	1	1
1	1	2	1	1	0	1	0	0	1	1	1	-1	1	1	0	0	1	1	1	0	0	0	0	0	1	1	1	0	0	1	2	1
1	1	0	1	-1	1	0	2	2	0	1	1	0	1	0	1	1	1	1	0	0	1	0	1	-1	0	1	0	0	1	1	0	1
1	1	1	0	1	1	2	1	0	2	1	0	0	1	0	1	1	1	1	0	0	1	1	1	1	0	0	1	1	0	1	1	1
2	1	1	1	1	1	2	0	-1	0	0	2	0	0	0	0	1	1	2	1	1	0	0	1	1	0	1	1	1	0	2	1	1
0	1	1	0	1	0	1	0	1	0	1	1	0	0	1	1	0	0	1	0	0	0	-1	1	-1	0	1	1	0	1	1	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	1	0	1	0	1	1	-1	0	-1	0	0	2	1	0	0	1	1	0	0	1	-1	-1	0	2	0	-1	1	0	1	1	0
1	1	0	0	0	1	1	1	-1	-1	1	1	0	1	1	1	1	0	1	1	0	-1	-1	-1	1	1	0	0	1	1	1	-1	0
1	1	1	0	1	1	1	1	-2	0	1	1	-1	0	1	0	0	1	1	1	-1	1	0	1	2	1	-1	1	0	0	2	2	2
1	0	0	0	0	1	1	0	-1	-1	0	1	0	0	0	1	1	1	1	1	1	2	2	1	0	1	1	2	1	1	-1	1	1
0	0	1	1	1	0	-1	0	-1	0	0	0	-1	0	1	1	1	1	0	0	0	0	0	1	1	1	0	1	0	1	1	1	1
0	0	-1	0	-2	1	1	1	-2	0	0	-1	0	0	-2	0	0	1	0	0	-2	1	1	-1	0	1	0	2	0	0	1	1	1
0	0	1	0	-1	-1	1	0	-1	0	1	0	1	0	0	-1	-1	1	1	1	-1	0	-1	2	1	1	-1	1	0	0	-1	2	2
2	1	1	1	1	1	2	2	1	1	1	2	1	2	2	2	2	2	2	2	2	2	2	2	0	2	2	2	2	2	2	2	2
1	2	1	1	1	0	1	1	0	0	2	0	1	1	0	2	1	0	1	1	0	2	1	1	1	1	0	2	0	1	1	2	2
1	0	0	0	0	1	1	0	1	0	0	-1	1	0	-1	-1	0	0	1	0	0	1	0	1	1	1	0	1	1	0	1	2	1
1	0	0	1	1	0	0	0	0	0	1	1	0	1	0	1	1	0	1	1	0	0	0	0	1	1	0	0	0	1	1	1	0
1	0	-1	-2	-1	1	1	0	-1	0	1	0	0	0	2	1	1	0	1	-2	-2	0	-1	1	1	2	0	1	0	1	0	2	1
2	2	1	0	1	1	2	1	-1	0	1	1	0	1	1	1	1	1	1	1	1	0	1	1	0	1	0	1	1	1	0	1	1
1	1	1	1	1	1	0	1	0	1	1	1	0	1	1	-1	1	1	1	1	1	1	1	0	1	1	1	1	1	1	1	1	1
0	0	0	0	1	0	-1	0	0	0	-1	0	0	0	0	1	1	0	1	1	1	1	1	0	0	1	1	1	1	0	0	0	2
1	-1	0	-1	0	0	0	1	0	0	-1	0	-1	1	1	-1	-1	1	1	1	0	1	1	2	-1	2	0	1	0	0	1	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	2	1	1	1	2	2	1	2	1	1	1	1	2	1	1	1	1	1	1	2	2	2	1	0	2	2	2	2	2	0	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
-2	-2	0	-2	-2	-2	0	-2	-2	-2	1	0	-1	-1	0	0	-1	0	-1	-1	0	0	0	1	1	2	-1	0	-1	0	0	0	2
1	0	1	0	1	1	1	2	-1	0	1	0	0	1	1	1	1	1	2	2	1	1	0	1	1	2	0	2	1	1	0	2	1
2	1	1	0	1	1	-1	-1	-2	1	-2	-2	-2	-1	1	1	1	0	1	-2	-1	-2	-1	0	2	1	0	0	-2	-2	1	0	1
0	1	2	0	0	1	0	1	0	1	0	1	1	0	1	-1	1	0	1	0	0	0	0	1	0	2	1	1	1	1	0	1	1
1	1	2	2	1	2	1	1	0	1	1	0	0	1	0	1	1	2	1	2	1	0	-1	0	-1	1	1	0	1	1	1	2	1
-1	-1	-1	-2	-1	-1	-1	-1	-1	-1	0	-1	0	1	0	1	0	1	0	-2	0	0	0	1	0	2	-1	0	0	2	2	2	2
1	0	1	0	0	1	1	0	1	1	-1	0	-1	0	1	1	1	-1	0	0	-1	0	-1	1	-2	2	0	0	-1	1	1	2	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
-1	0	1	-1	-1	-2	-1	-2	1	0	-2	-1	-2	0	1	1	0	-2	-1	0	-2	1	-1	1	0	2	-2	1	-1	0	1	2	1
0	0	1	0	0	0	0	0	1	1	0	-1	0	0	0	0	0	0	1	2	-1	-1	-1	0	0	1	-1	-1	0	1	1	1	2
1	1	1	1	1	1	0	0	1	1	1	1	1	1	0	1	1	1	1	0	1	1	1	0	-1	1	0	1	1	1	1	1	2
1	0	1	1	1	0	1	0	1	1	1	0	0	0	0	1	1	1	1	1	0	1	1	2	0	1	0	1	0	1	1	0	2
2	2	1	1	1	1	1	NA	0	1	2	1	1	1	1	-1	0	1	2	1	1	0	1	1	2	-1	0	1	1	2	0	0	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	1	0	1	0	1	1	-1	0	1	1	0	0	0	0	0	1	1	1	2	1	1	0	1	1	0	0	1	0	0	1	1
1	1	1	0	1	2	1	0	0	0	1	1	-1	0	1	1	1	2	1	1	0	1	0	1	1	1	0	0	1	1	2	1	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
2	1	1	1	1	1	2	1	-1	0	1	1	1	1	1	0	0	-1	1	1	1	1	1	1	1	1	1	1	1	1	1	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	1	1	1	1	1	1	1	0	1	0	0	1	0	1	1	1	1	1	1	1	0	0	0	0	0	1	0	1	0	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	1	0	0	1	0	0	-1	0	2	0	0	0	0	1	1	1	1	1	2	1	-1	1	1	2	2	1	1	0	0	1	1
1	1	0	0	-1	0	-1	0	-1	0	1	0	0	0	1	0	-1	2	1	1	0	1	0	1	-1	0	2	1	1	1	1	1	0
0	-1	0	2	-1	-2	0	0	0	0	0	-1	2	0	0	-1	-1	0	-1	-1	0	0	1	0	0	1	0	0	-1	-1	0	1	0
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	2	2	1	1	2	1	1	0	1	1	2	1	1	2	1	1	2	2	2	0	1	0	0	1	2	2	1	1	2	-1	2	2
0	0	0	0	0	-1	0	0	-1	0	-1	0	-1	0	1	1	0	1	0	1	1	0	-1	0	1	1	0	1	0	0	1	1	1
1	1	1	0	1	1	0	1	0	1	1	0	1	0	1	0	0	0	1	1	0	0	0	1	1	0	0	0	0	1	0	2	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
0	0	0	0	0	-1	0	1	-1	0	1	0	-1	0	1	-1	-1	0	0	0	-1	1	0	1	-1	1	-1	0	-1	-1	1	1	2
0	0	0	0	0	-1	-1	0	-1	-1	0	0	0	1	0	-1	-1	0	-1	0	-1	1	0	-1	-1	0	-1	0	0	0	-1	0	1
0	1	1	0	1	0	1	0	-1	0	0	1	0	1	1	0	0	1	1	0	0	0	0	0	0	1	0	1	1	0	0	2	1
2	1	2	0	1	1	0	1	1	-1	1	-1	1	2	0	0	0	2	2	1	2	1	1	1	1	0	2	1	0	1	-1	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
0	0	0	-1	0	0	-1	-1	-1	0	0	-1	-1	0	-1	0	0	1	0	0	-1	-1	-1	0	2	1	-1	-1	0	0	0	1	1
0	1	0	0	0	1	1	0	0	-1	1	0	0	1	0	1	1	1	1	0	0	0	0	1	1	1	0	1	1	0	2	1	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
0	0	0	-1	-1	0	-1	0	1	1	1	0	0	-1	2	-1	-1	1	-1	0	-1	0	-1	1	0	1	-1	0	0	-1	-1	1	1
-2	-2	-1	-1	-1	-2	-1	0	1	1	1	-2	-1	-1	1	-1	0	1	-1	0	NA	0	-2	0	2	0	-2	1	0	1	0	1	1
1	1	0	0	1	1	1	0	1	0	1	1	0	1	1	0	1	0	1	1	1	1	1	0	-1	1	0	1	1	0	1	0	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	1	1	1	1	-1	2	0	-1	1	-1	0	1	1	1	0	1	1	0	-2	0	-1	1	0	1	0	0	0	1	1	1	1
0	1	1	1	0	0	0	1	1	-1	2	0	0	1	1	-1	-1	-1	1	1	0	-1	0	0	-2	0	0	0	0	0	1	2	1
0	0	0	-1	-1	0	-1	-1	0	-2	0	-1	-1	-2	0	0	0	1	0	0	1	1	0	1	2	2	0	1	0	0	-2	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
-2	-1	-2	-2	-2	-1	-1	-2	-2	-1	-2	-2	-2	-2	-2	0	0	-1	-2	-1	-2	0	-1	-1	-1	0	-1	0	-1	0	1	0	0
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	0	-2	-1	-2	1	1	-1	0	1	1	-1	-1	-1	2	2	1	2	1	1	-2	2	1	2	2	2	-2	2	2	2	2	2	2
2	2	2	1	1	2	0	1	0	0	0	1	0	1	1	1	2	2	1	1	2	2	2	2	2	2	1	2	1	1	0	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	0	0	0	1	-1	2	-1	0	0	0	-1	0	1	1	1	2	1	0	0	1	0	1	2	2	0	1	0	0	2	2	2
1	1	0	0	0	1	-1	2	1	1	0	2	1	2	1	-1	-1	1	1	2	2	0	1	1	0	1	1	2	1	0	-1	0	1
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	0	1	0	1	0	1	0	NA	1	1	0	2	1	2	1	1	1	1	0	1	2	1	0	1	2	1	2	1	1	1	2	1
1	0	1	1	1	1	-1	0	1	1	1	-1	0	1	0	-1	-1	0	1	2	2	2	1	1	2	1	0	2	1	1	1	2	2
NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
1	1	0	1	1	0	0	0	1	0	1	0	1	1	-1	0	0	1	1	0	0	1	1	1	2	1	0	1	0	0	1	2	2

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 11 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14334&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]11 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14334&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14334&T=0

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	11 seconds
R Server	'Gwilym Jenkins' @ 72.249.127.135

Summary of survey scores (median of Likert score was subtracted)
Question	mean	Sum ofpositives (Ps)	Sum ofnegatives (Ns)	(Ps-Ns)/(Ps+Ns)	Count ofpositives (Pc)	Count ofnegatives (Nc)	(Pc-Nc)/(Pc+Nc)
1	0.85	182	12	0.88	150	9	0.89
2	0.74	164	15	0.83	135	13	0.82
3	0.76	167	14	0.85	139	12	0.84
4	0.39	106	28	0.58	93	22	0.62
5	0.51	128	26	0.66	113	22	0.67
6	0.7	164	25	0.74	132	21	0.73
7	0.63	155	29	0.68	123	29	0.62
8	0.52	129	25	0.68	108	21	0.67
9	-0.17	54	88	-0.24	46	78	-0.26
10	0.34	96	29	0.54	87	27	0.53
11	0.48	121	25	0.66	111	20	0.69
12	0.4	110	29	0.58	96	26	0.57
13	0.04	57	49	0.08	54	40	0.15
14	0.5	122	22	0.69	108	19	0.7
15	0.64	148	19	0.77	133	17	0.77
16	0.46	133	41	0.53	115	37	0.51
17	0.38	111	36	0.51	100	32	0.52
18	0.78	171	16	0.83	145	14	0.82
19	0.88	187	11	0.89	160	10	0.88
20	0.66	167	35	0.65	117	28	0.61
21	0.57	148	34	0.63	113	27	0.61
22	0.71	158	16	0.82	125	14	0.8
23	0.38	116	40	0.49	97	37	0.45
24	0.78	166	10	0.89	141	10	0.87
25	0.42	128	43	0.5	105	36	0.49
26	1.12	231	7	0.94	169	6	0.93
27	0.49	126	33	0.58	93	28	0.54
28	0.98	202	5	0.95	149	5	0.94
29	0.55	130	21	0.72	111	19	0.71
30	0.7	148	8	0.9	132	7	0.9
31	0.64	152	24	0.73	133	22	0.72
32	1.07	220	5	0.96	165	4	0.95
33	0.88	192	15	0.86	147	15	0.81

\begin{tabular}{lllllllll}
\hline
Summary of survey scores (median of Likert score was subtracted) \tabularnewline
Question & mean & Sum ofpositives (Ps) & Sum ofnegatives (Ns) & (Ps-Ns)/(Ps+Ns) & Count ofpositives (Pc) & Count ofnegatives (Nc) & (Pc-Nc)/(Pc+Nc) \tabularnewline
1 & 0.85 & 182 & 12 & 0.88 & 150 & 9 & 0.89 \tabularnewline
2 & 0.74 & 164 & 15 & 0.83 & 135 & 13 & 0.82 \tabularnewline
3 & 0.76 & 167 & 14 & 0.85 & 139 & 12 & 0.84 \tabularnewline
4 & 0.39 & 106 & 28 & 0.58 & 93 & 22 & 0.62 \tabularnewline
5 & 0.51 & 128 & 26 & 0.66 & 113 & 22 & 0.67 \tabularnewline
6 & 0.7 & 164 & 25 & 0.74 & 132 & 21 & 0.73 \tabularnewline
7 & 0.63 & 155 & 29 & 0.68 & 123 & 29 & 0.62 \tabularnewline
8 & 0.52 & 129 & 25 & 0.68 & 108 & 21 & 0.67 \tabularnewline
9 & -0.17 & 54 & 88 & -0.24 & 46 & 78 & -0.26 \tabularnewline
10 & 0.34 & 96 & 29 & 0.54 & 87 & 27 & 0.53 \tabularnewline
11 & 0.48 & 121 & 25 & 0.66 & 111 & 20 & 0.69 \tabularnewline
12 & 0.4 & 110 & 29 & 0.58 & 96 & 26 & 0.57 \tabularnewline
13 & 0.04 & 57 & 49 & 0.08 & 54 & 40 & 0.15 \tabularnewline
14 & 0.5 & 122 & 22 & 0.69 & 108 & 19 & 0.7 \tabularnewline
15 & 0.64 & 148 & 19 & 0.77 & 133 & 17 & 0.77 \tabularnewline
16 & 0.46 & 133 & 41 & 0.53 & 115 & 37 & 0.51 \tabularnewline
17 & 0.38 & 111 & 36 & 0.51 & 100 & 32 & 0.52 \tabularnewline
18 & 0.78 & 171 & 16 & 0.83 & 145 & 14 & 0.82 \tabularnewline
19 & 0.88 & 187 & 11 & 0.89 & 160 & 10 & 0.88 \tabularnewline
20 & 0.66 & 167 & 35 & 0.65 & 117 & 28 & 0.61 \tabularnewline
21 & 0.57 & 148 & 34 & 0.63 & 113 & 27 & 0.61 \tabularnewline
22 & 0.71 & 158 & 16 & 0.82 & 125 & 14 & 0.8 \tabularnewline
23 & 0.38 & 116 & 40 & 0.49 & 97 & 37 & 0.45 \tabularnewline
24 & 0.78 & 166 & 10 & 0.89 & 141 & 10 & 0.87 \tabularnewline
25 & 0.42 & 128 & 43 & 0.5 & 105 & 36 & 0.49 \tabularnewline
26 & 1.12 & 231 & 7 & 0.94 & 169 & 6 & 0.93 \tabularnewline
27 & 0.49 & 126 & 33 & 0.58 & 93 & 28 & 0.54 \tabularnewline
28 & 0.98 & 202 & 5 & 0.95 & 149 & 5 & 0.94 \tabularnewline
29 & 0.55 & 130 & 21 & 0.72 & 111 & 19 & 0.71 \tabularnewline
30 & 0.7 & 148 & 8 & 0.9 & 132 & 7 & 0.9 \tabularnewline
31 & 0.64 & 152 & 24 & 0.73 & 133 & 22 & 0.72 \tabularnewline
32 & 1.07 & 220 & 5 & 0.96 & 165 & 4 & 0.95 \tabularnewline
33 & 0.88 & 192 & 15 & 0.86 & 147 & 15 & 0.81 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14334&T=1

[TABLE]
[ROW][C]Summary of survey scores (median of Likert score was subtracted)[/C][/ROW]
[ROW][C]Question[/C][C]mean[/C][C]Sum ofpositives (Ps)[/C][C]Sum ofnegatives (Ns)[/C][C](Ps-Ns)/(Ps+Ns)[/C][C]Count ofpositives (Pc)[/C][C]Count ofnegatives (Nc)[/C][C](Pc-Nc)/(Pc+Nc)[/C][/ROW]
[ROW][C]1[/C][C]0.85[/C][C]182[/C][C]12[/C][C]0.88[/C][C]150[/C][C]9[/C][C]0.89[/C][/ROW]
[ROW][C]2[/C][C]0.74[/C][C]164[/C][C]15[/C][C]0.83[/C][C]135[/C][C]13[/C][C]0.82[/C][/ROW]
[ROW][C]3[/C][C]0.76[/C][C]167[/C][C]14[/C][C]0.85[/C][C]139[/C][C]12[/C][C]0.84[/C][/ROW]
[ROW][C]4[/C][C]0.39[/C][C]106[/C][C]28[/C][C]0.58[/C][C]93[/C][C]22[/C][C]0.62[/C][/ROW]
[ROW][C]5[/C][C]0.51[/C][C]128[/C][C]26[/C][C]0.66[/C][C]113[/C][C]22[/C][C]0.67[/C][/ROW]
[ROW][C]6[/C][C]0.7[/C][C]164[/C][C]25[/C][C]0.74[/C][C]132[/C][C]21[/C][C]0.73[/C][/ROW]
[ROW][C]7[/C][C]0.63[/C][C]155[/C][C]29[/C][C]0.68[/C][C]123[/C][C]29[/C][C]0.62[/C][/ROW]
[ROW][C]8[/C][C]0.52[/C][C]129[/C][C]25[/C][C]0.68[/C][C]108[/C][C]21[/C][C]0.67[/C][/ROW]
[ROW][C]9[/C][C]-0.17[/C][C]54[/C][C]88[/C][C]-0.24[/C][C]46[/C][C]78[/C][C]-0.26[/C][/ROW]
[ROW][C]10[/C][C]0.34[/C][C]96[/C][C]29[/C][C]0.54[/C][C]87[/C][C]27[/C][C]0.53[/C][/ROW]
[ROW][C]11[/C][C]0.48[/C][C]121[/C][C]25[/C][C]0.66[/C][C]111[/C][C]20[/C][C]0.69[/C][/ROW]
[ROW][C]12[/C][C]0.4[/C][C]110[/C][C]29[/C][C]0.58[/C][C]96[/C][C]26[/C][C]0.57[/C][/ROW]
[ROW][C]13[/C][C]0.04[/C][C]57[/C][C]49[/C][C]0.08[/C][C]54[/C][C]40[/C][C]0.15[/C][/ROW]
[ROW][C]14[/C][C]0.5[/C][C]122[/C][C]22[/C][C]0.69[/C][C]108[/C][C]19[/C][C]0.7[/C][/ROW]
[ROW][C]15[/C][C]0.64[/C][C]148[/C][C]19[/C][C]0.77[/C][C]133[/C][C]17[/C][C]0.77[/C][/ROW]
[ROW][C]16[/C][C]0.46[/C][C]133[/C][C]41[/C][C]0.53[/C][C]115[/C][C]37[/C][C]0.51[/C][/ROW]
[ROW][C]17[/C][C]0.38[/C][C]111[/C][C]36[/C][C]0.51[/C][C]100[/C][C]32[/C][C]0.52[/C][/ROW]
[ROW][C]18[/C][C]0.78[/C][C]171[/C][C]16[/C][C]0.83[/C][C]145[/C][C]14[/C][C]0.82[/C][/ROW]
[ROW][C]19[/C][C]0.88[/C][C]187[/C][C]11[/C][C]0.89[/C][C]160[/C][C]10[/C][C]0.88[/C][/ROW]
[ROW][C]20[/C][C]0.66[/C][C]167[/C][C]35[/C][C]0.65[/C][C]117[/C][C]28[/C][C]0.61[/C][/ROW]
[ROW][C]21[/C][C]0.57[/C][C]148[/C][C]34[/C][C]0.63[/C][C]113[/C][C]27[/C][C]0.61[/C][/ROW]
[ROW][C]22[/C][C]0.71[/C][C]158[/C][C]16[/C][C]0.82[/C][C]125[/C][C]14[/C][C]0.8[/C][/ROW]
[ROW][C]23[/C][C]0.38[/C][C]116[/C][C]40[/C][C]0.49[/C][C]97[/C][C]37[/C][C]0.45[/C][/ROW]
[ROW][C]24[/C][C]0.78[/C][C]166[/C][C]10[/C][C]0.89[/C][C]141[/C][C]10[/C][C]0.87[/C][/ROW]
[ROW][C]25[/C][C]0.42[/C][C]128[/C][C]43[/C][C]0.5[/C][C]105[/C][C]36[/C][C]0.49[/C][/ROW]
[ROW][C]26[/C][C]1.12[/C][C]231[/C][C]7[/C][C]0.94[/C][C]169[/C][C]6[/C][C]0.93[/C][/ROW]
[ROW][C]27[/C][C]0.49[/C][C]126[/C][C]33[/C][C]0.58[/C][C]93[/C][C]28[/C][C]0.54[/C][/ROW]
[ROW][C]28[/C][C]0.98[/C][C]202[/C][C]5[/C][C]0.95[/C][C]149[/C][C]5[/C][C]0.94[/C][/ROW]
[ROW][C]29[/C][C]0.55[/C][C]130[/C][C]21[/C][C]0.72[/C][C]111[/C][C]19[/C][C]0.71[/C][/ROW]
[ROW][C]30[/C][C]0.7[/C][C]148[/C][C]8[/C][C]0.9[/C][C]132[/C][C]7[/C][C]0.9[/C][/ROW]
[ROW][C]31[/C][C]0.64[/C][C]152[/C][C]24[/C][C]0.73[/C][C]133[/C][C]22[/C][C]0.72[/C][/ROW]
[ROW][C]32[/C][C]1.07[/C][C]220[/C][C]5[/C][C]0.96[/C][C]165[/C][C]4[/C][C]0.95[/C][/ROW]
[ROW][C]33[/C][C]0.88[/C][C]192[/C][C]15[/C][C]0.86[/C][C]147[/C][C]15[/C][C]0.81[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14334&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14334&T=1

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Summary of survey scores (median of Likert score was subtracted)
Question	mean	Sum ofpositives (Ps)	Sum ofnegatives (Ns)	(Ps-Ns)/(Ps+Ns)	Count ofpositives (Pc)	Count ofnegatives (Nc)	(Pc-Nc)/(Pc+Nc)
1	0.85	182	12	0.88	150	9	0.89
2	0.74	164	15	0.83	135	13	0.82
3	0.76	167	14	0.85	139	12	0.84
4	0.39	106	28	0.58	93	22	0.62
5	0.51	128	26	0.66	113	22	0.67
6	0.7	164	25	0.74	132	21	0.73
7	0.63	155	29	0.68	123	29	0.62
8	0.52	129	25	0.68	108	21	0.67
9	-0.17	54	88	-0.24	46	78	-0.26
10	0.34	96	29	0.54	87	27	0.53
11	0.48	121	25	0.66	111	20	0.69
12	0.4	110	29	0.58	96	26	0.57
13	0.04	57	49	0.08	54	40	0.15
14	0.5	122	22	0.69	108	19	0.7
15	0.64	148	19	0.77	133	17	0.77
16	0.46	133	41	0.53	115	37	0.51
17	0.38	111	36	0.51	100	32	0.52
18	0.78	171	16	0.83	145	14	0.82
19	0.88	187	11	0.89	160	10	0.88
20	0.66	167	35	0.65	117	28	0.61
21	0.57	148	34	0.63	113	27	0.61
22	0.71	158	16	0.82	125	14	0.8
23	0.38	116	40	0.49	97	37	0.45
24	0.78	166	10	0.89	141	10	0.87
25	0.42	128	43	0.5	105	36	0.49
26	1.12	231	7	0.94	169	6	0.93
27	0.49	126	33	0.58	93	28	0.54
28	0.98	202	5	0.95	149	5	0.94
29	0.55	130	21	0.72	111	19	0.71
30	0.7	148	8	0.9	132	7	0.9
31	0.64	152	24	0.73	133	22	0.72
32	1.07	220	5	0.96	165	4	0.95
33	0.88	192	15	0.86	147	15	0.81

Pearson correlation matrix of survey scores
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1	0.938805272754048	0.92477786859072
(Ps-Ns)/(Ps+Ns)	0.938805272754048	1	0.99494760740329
(Pc-Nc)/(Pc+Nc)	0.92477786859072	0.99494760740329	1

\begin{tabular}{lllllllll}
\hline
Pearson correlation matrix of survey scores \tabularnewline
 & mean & (Ps-Ns)/(Ps+Ns) & (Pc-Nc)/(Pc+Nc) \tabularnewline
mean & 1 & 0.938805272754048 & 0.92477786859072 \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.938805272754048 & 1 & 0.99494760740329 \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.92477786859072 & 0.99494760740329 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14334&T=2

[TABLE]
[ROW][C]Pearson correlation matrix of survey scores[/C][/ROW]
[ROW][C][/C][C]mean[/C][C](Ps-Ns)/(Ps+Ns)[/C][C](Pc-Nc)/(Pc+Nc)[/C][/ROW]
[ROW][C]mean[/C][C]1[/C][C]0.938805272754048[/C][C]0.92477786859072[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.938805272754048[/C][C]1[/C][C]0.99494760740329[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.92477786859072[/C][C]0.99494760740329[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14334&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14334&T=2

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Pearson correlation matrix of survey scores
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1	0.938805272754048	0.92477786859072
(Ps-Ns)/(Ps+Ns)	0.938805272754048	1	0.99494760740329
(Pc-Nc)/(Pc+Nc)	0.92477786859072	0.99494760740329	1

Kendall tau rank correlation matrix of survey scores
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1	0.827587725496699	0.777459762444908
(Ps-Ns)/(Ps+Ns)	0.827587725496699	1	0.949286208462938
(Pc-Nc)/(Pc+Nc)	0.777459762444908	0.949286208462938	1

\begin{tabular}{lllllllll}
\hline
Kendall tau rank correlation matrix of survey scores \tabularnewline
 & mean & (Ps-Ns)/(Ps+Ns) & (Pc-Nc)/(Pc+Nc) \tabularnewline
mean & 1 & 0.827587725496699 & 0.777459762444908 \tabularnewline
(Ps-Ns)/(Ps+Ns) & 0.827587725496699 & 1 & 0.949286208462938 \tabularnewline
(Pc-Nc)/(Pc+Nc) & 0.777459762444908 & 0.949286208462938 & 1 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=14334&T=3

[TABLE]
[ROW][C]Kendall tau rank correlation matrix of survey scores[/C][/ROW]
[ROW][C][/C][C]mean[/C][C](Ps-Ns)/(Ps+Ns)[/C][C](Pc-Nc)/(Pc+Nc)[/C][/ROW]
[ROW][C]mean[/C][C]1[/C][C]0.827587725496699[/C][C]0.777459762444908[/C][/ROW]
[ROW][C](Ps-Ns)/(Ps+Ns)[/C][C]0.827587725496699[/C][C]1[/C][C]0.949286208462938[/C][/ROW]
[ROW][C](Pc-Nc)/(Pc+Nc)[/C][C]0.777459762444908[/C][C]0.949286208462938[/C][C]1[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=14334&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=14334&T=3

As an alternative you can also use a QR Code:

The GUIDs for individual cells are displayed in the table below:

Kendall tau rank correlation matrix of survey scores
	mean	(Ps-Ns)/(Ps+Ns)	(Pc-Nc)/(Pc+Nc)
mean	1	0.827587725496699	0.777459762444908
(Ps-Ns)/(Ps+Ns)	0.827587725496699	1	0.949286208462938
(Pc-Nc)/(Pc+Nc)	0.777459762444908	0.949286208462938	1

Parameters (Session):

par1 = -2 -1 0 1 2 ;

Parameters (R input):

par1 = -2 -1 0 1 2 ;

R code (references can be found in the software module):

x <- t(x)
nx <- length(x[,1])
cx <- length(x[1,])
mymedian <- median(as.numeric(strsplit(par1,' ')[[1]]))
myresult <- array(NA, dim = c(cx,7))
rownames(myresult) <- paste('Q',1:cx,sep='')
colnames(myresult) <- c('mean','Sum of
positives (Ps)','Sum of
negatives (Ns)', '(Ps-Ns)/(Ps+Ns)', 'Count of
positives (Pc)', 'Count of
negatives (Nc)', '(Pc-Nc)/(Pc+Nc)')
for (i in 1:cx) {
spos <- 0
sneg <- 0
cpos <- 0
cneg <- 0
for (j in 1:nx) {
if (!is.na(x[j,i])) {
myx <- as.numeric(x[j,i]) - mymedian
if (myx > 0) {
spos = spos + myx
cpos = cpos + 1
}
if (myx < 0) {
sneg = sneg + abs(myx)
cneg = cneg + 1
}
}
}
myresult[i,1] <- round(mean(as.numeric(x[,i]),na.rm=T)-mymedian,2)
myresult[i,2] <- spos
myresult[i,3] <- sneg
myresult[i,4] <- round((spos - sneg) / (spos + sneg),2)
myresult[i,5] <- cpos
myresult[i,6] <- cneg
myresult[i,7] <- round((cpos - cneg) / (cpos + cneg),2)
}
myresult
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Summary of survey scores (median of Likert score was subtracted)',8,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Question',header=TRUE)
for (i in 1:7) {
a<-table.element(a,colnames(myresult)[i],header=TRUE)
}
a<-table.row.end(a)
for (i in 1:cx) {
a<-table.row.start(a)
a<-table.element(a,i,header=TRUE)
for (j in 1:7) {
a<-table.element(a,myresult[i,j])
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Pearson correlation matrix of survey scores',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,'(Ps-Ns)/(Ps+Ns)',header=TRUE)
a<-table.element(a,'(Pc-Nc)/(Pc+Nc)',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,cor(myresult[,1],myresult[,1],method='pearson'))
a<-table.element(a,cor(myresult[,1],myresult[,4],method='pearson'))
a<-table.element(a,cor(myresult[,1],myresult[,7],method='pearson'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(Ps-Ns)/(Ps+Ns)',header=TRUE)
a<-table.element(a,cor(myresult[,4],myresult[,1],method='pearson'))
a<-table.element(a,cor(myresult[,4],myresult[,4],method='pearson'))
a<-table.element(a,cor(myresult[,4],myresult[,7],method='pearson'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(Pc-Nc)/(Pc+Nc)',header=TRUE)
a<-table.element(a,cor(myresult[,7],myresult[,1],method='pearson'))
a<-table.element(a,cor(myresult[,7],myresult[,4],method='pearson'))
a<-table.element(a,cor(myresult[,7],myresult[,7],method='pearson'))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Kendall tau rank correlation matrix of survey scores',4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'',header=TRUE)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,'(Ps-Ns)/(Ps+Ns)',header=TRUE)
a<-table.element(a,'(Pc-Nc)/(Pc+Nc)',header=TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'mean',header=TRUE)
a<-table.element(a,cor(myresult[,1],myresult[,1],method='kendall'))
a<-table.element(a,cor(myresult[,1],myresult[,4],method='kendall'))
a<-table.element(a,cor(myresult[,1],myresult[,7],method='kendall'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(Ps-Ns)/(Ps+Ns)',header=TRUE)
a<-table.element(a,cor(myresult[,4],myresult[,1],method='kendall'))
a<-table.element(a,cor(myresult[,4],myresult[,4],method='kendall'))
a<-table.element(a,cor(myresult[,4],myresult[,7],method='kendall'))
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'(Pc-Nc)/(Pc+Nc)',header=TRUE)
a<-table.element(a,cor(myresult[,7],myresult[,1],method='kendall'))
a<-table.element(a,cor(myresult[,7],myresult[,4],method='kendall'))
a<-table.element(a,cor(myresult[,7],myresult[,7],method='kendall'))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code